外观
论文arXiv 2610.0928321 天前
LACE-CRAFT：基于 Actor 继承与 Blackboard 协作的机器人协同设计
原文链接：LACE-CRAFT: Robot Co-Design with Actor Inheritance and Blackboard Collaboration（arXiv 2610.09283 · 发布日期：2026-10-07）
LACE-CRAFT: Robot Co-Design with Actor Inheritance and Blackboard Collaboration
LACE-CRAFT面向形态搜索与策略学习耦合中的经验丢失，通过策略继承和黑板协作，在五个运动基准和原型上验证。
核心要点
- 问题：机器人协同设计每个新设计从头训练，会丢弃已获得的控制经验。
- 方法：LACE恢复当前最优的完整学习状态，并用其actor参数和观测统计初始化兼容挑战者。
- 协作：CRAFT用共享实验记录和行为回放，协调反馈、形态、奖励与集成角色生成并互审提案。
- 实验：五个运动基准中，三次评估种子的平均分比D2C高6.4–91.9%；原型展示室内行走。
验证范围与局限：验证范围包括五个运动基准、五任务消融，以及一个制造原型的室内行走演示。
英文摘要 (Abstract)
Robot co-design couples morphology search with policy learning, yet training every new design from scratch discards acquired control experience. We present LACE-CRAFT, which compares continued learning on the current robot with policy adaptation to new morphology-reward pairs. LACE resumes the incumbent's full learning state and initializes compatible challengers with its actor parameters and observation statistics. A fixed task metric selects among both branches and the frozen incumbent. CRAFT coordinates Feedback, Morphology, Reward, and Integration roles through shared experimental records and behavioral replays to generate and cross-review paired proposals. A generative extension converts generated meshes into editable articulated models with configured joints, actuator interfaces, and consistently updated simulation assets. Across five locomotion benchmarks, mean scores over three evaluation seeds are 6.4-91.9% higher than D2C. Both methods train 30 new morphology-reward pairs over five rounds; LACE additionally uses four continuation training units. Five-task ablations examine policy inheritance and replay-derived feedback. A fabricated prototype demonstrates indoor walking and illustrates the geometry-to-hardware workflow.
作者：Yuhan Wen, Jiawei Wang, Qixuan Zhang, Calvin Zhang, Yusen Qin, Lan Xu
来源
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